The general usefulness of reduced models follows from their reduced
number of variables and parameters. A reduced model can be
more easily simulated, analysed and learned from data.
Beyond these benefits, the model reduction process
unfolds useful information about the full model.
First, it provides a classification of the parameters,
according to their identifiability, that is very useful for machine
learning applications [1]}, [2]}.
Parameters of the full model, not occurring in the reduced model
are sloppy in the sense of a lack of sensibility of model
properties with respect to them. Other parameters of the
full model, occurring in the reduced model in a grouped
manner, for instance as monomials, are not identifiable
independently.
The reduction process also outputs timescales of different
variables.
This is important for understanding the dynamics of the system and
in certain cases can be used to gain biological understanding.
In particular, slow variables are involved into
memory mechanisms, important in learning processes and for the maintenance of the biological identity, whereas
fast variables are important for complex
responses needed for
adaptation to external changes.
